Brain Segmentation
60 papers with code • 1 benchmarks • 4 datasets
Benchmarks
These leaderboards are used to track progress in Brain Segmentation
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Libraries
Use these libraries to find Brain Segmentation models and implementations
CrossMoDA
BRATS 2021
Tc1 Mouse cerebellum atlas
Multi-template MRI mouse brain atlas
Latest papers
FAST-AID Brain: Fast and Accurate Segmentation Tool using Artificial Intelligence Developed for Brain
A novel deep learning method is proposed for fast and accurate segmentation of the human brain into 132 regions.
An Open-Source Tool for Longitudinal Whole-Brain and White Matter Lesion Segmentation
In this paper we describe and validate a longitudinal method for whole-brain segmentation of longitudinal MRI scans.
An automatic pipeline for atlas-based fetal and neonatal brain segmentation and analysis
The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications.
Deep Learning Framework for Real-time Fetal Brain Segmentation in MRI
bchimagine/real_time_fetal_brain_segmentation
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Fast and accurate segmentation of the fetal brain on fetal MRI is required to achieve real-time fetal head pose estimation and motion tracking for slice re-acquisition and steering.
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
The aim was to automatically perform unilateral VS and bilateral cochlea segmentation on hrT2 as provided in the testing set (N=137).
Factorisation-based Image Labelling
Segmentation of brain magnetic resonance images (MRI) into anatomical regions is a useful task in neuroimaging.
Partial supervision for the FeTA challenge 2021
lucasfidon/trabit_brats2021
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Label-set loss functions allow to train deep neural networks with partially segmented images, i. e. segmentations in which some classes may be grouped into super-classes.
Whole Brain Segmentation with Full Volume Neural Network
To address these issues, we propose to adopt a full volume framework, which feeds the full volume brain image into the segmentation network and directly outputs the segmentation result for the whole brain volume.
DBSegment: Fast and robust segmentation of deep brain structures -- Evaluation of transportability across acquisition domains
We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach.
AGD-Autoencoder: Attention Gated Deep Convolutional Autoencoder for Brain Tumor Segmentation
In this paper, we propose a novel attention gate (AG model) for brain tumor segmentation that utilizes both the edge detecting unit and the attention gated network to highlight and segment the salient regions from fMRI images.
